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Kaur, Chamandeep
- Classification of Human Emotions using Multiwavelet Transform based Features and Random Forest Technique
Abstract Views :148 |
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, UIET, Panjab University, Chandigarh - 160014, Punjab, IN
1 Department of Electronics and Communication Engineering, UIET, Panjab University, Chandigarh - 160014, Punjab, IN
Source
Indian Journal of Science and Technology, Vol 8, No 28 (2015), Pagination:Abstract
Objective: To extract statistical features from EEG signal for human emotion recognition. Methods: Emotions, the “inner” state of a person play a vital role in analysing the state of mind. In this paper, a new method for human emotion recognition using Multi Wavelet Transform (MWT) and random forest (ensemble technique) for classification of human emotions from EEG signals has been proposed. The experiment is conducted on EEG database for emotions i.e., DEAP data set. In present work, the data set used contains EEG recording from 4 participants (S01, S02, S03, S04) from 15 channel (FP1, FP2, F3, P3, F4, T7, T8, P4, O1, PZ, PO3, O2, P7, CP2, and C4) having 40 trails each. The paper explore the capability of proposed features namely Mean, Standard deviation, Variance, Shannon entropy, Hjorth parameters and Band power. Findings: The feature set obtained using Multi-Wavelet are then used as input for MLP, KNN, MC-SVM with Puk kernel function and Random Forest (ensemble) classifier for the classification of emotions. The classification accuracy for different emotion state happy 99.8%, sad 98.3%, exciting 95%, hate 96.4% are obtained by the proposed method. Overall 98.1% for classification of emotions (happy, sad, exciting, hate) from EEG signals. Application: The findings are helpful in monitoring alertness, cognitive engagement of patients with impaired motor functions; recognition of emotions; investigating sleep disorders and physiology. In future, efficient feature extraction algorithm using different multiwavelet functions and with a different set of features which include the temporal dynamics of brain signals in the human cognitive system can be considered.Keywords
Band Power, Hjorth Parameters, Human Computer Interface (HCI), Multi-Wavelet Transform (MWT), Random Forest, Shannon Entropy, Statistical ParametersFull Text
- Image and Video Anomaly Detection Using AI Based Deepanomaly Detectors
Abstract Views :52 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, IN
2 Department of Electronics and Communication, Nitte Meenakshi Institute of Technology, IN
3 Department of Mechanical Engineering, Pravara Rural Engineering College, IN
4 Department of Information Technology, M. Kumarasamy College of Engineering, IN
5 Department of Computer Science and Information Technology, Jazan University,, SA
1 Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, IN
2 Department of Electronics and Communication, Nitte Meenakshi Institute of Technology, IN
3 Department of Mechanical Engineering, Pravara Rural Engineering College, IN
4 Department of Information Technology, M. Kumarasamy College of Engineering, IN
5 Department of Computer Science and Information Technology, Jazan University,, SA
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 2 (2023), Pagination: 3161-3167Abstract
In computer vision and anomaly detection, this research delves into the application of AI-based Deep Anomaly Detectors for the identification of anomalies in images and videos. The escalating growth of digital content necessitates robust and efficient methods for anomaly detection to ensure the integrity and security of visual data. As the volume of visual data continues to surge, conventional anomaly detection methods fall short in addressing the complexities inherent in images and videos. Traditional anomaly detection methods often struggle with the nuanced patterns and variations present in images and videos. The need for a more sophisticated and adaptive approach becomes imperative to identify anomalies accurately amidst the vast and diverse landscape of visual data. This study addresses this gap by leveraging the power of artificial intelligence, specifically Deep Anomaly Detectors, to enhance the accuracy and speed of anomaly detection in visual content. This research aims to bridge this gap by proposing a novel methodology that combines deep learning techniques with anomaly detection to achieve superior results in identifying anomalies in visual content. The proposed methodology involves the utilization of state-of-the-art deep learning architectures, training on a diverse dataset of images and videos to capture intricate patterns associated with anomalies. The model is then fine-tuned to enhance its sensitivity to deviations from normal visual patterns, ensuring a robust anomaly detection system. The results showcase a significant improvement in anomaly detection accuracy compared to traditional methods. The AI-based Deep Anomaly Detector exhibits a high level of sensitivity and specificity, effectively distinguishing anomalies in real-world scenarios, thus validating the efficacy of the proposed method.Keywords
Anomaly Detection, Deep Learning, Image Analysis, Computer Vision, Video Processing
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